Study Area

Methods

Describe process for delineating catchments to use as assessment areas.

Metric Descritpions

Wetland Quantity

This metric assigns points based on the percentage of the catchment mapped as wetland. The score breakpoints are:

50% or greater - 10 points
25% to 50% - 7 points
10% to 25% - 5 points
5% to 10% - 3 points
1% to 5% - 1 point

Wetland Quality

Points are assigned according to the weighted average ORAM score/category within a catchment. Example: Wetland A is 3 acres with an ORAM score of 50, Wetland B is 2 acres with a score of 75 and Wetland C is 2 acres with a score of 60. Next, multiply each wetland acreage by its ORAM score and add the products together: (50x3) + (75x2) + (60x2) = 420. Finally, divided by the total acreage to get the weighted average score: 420/7 = 60. The weighted average ORAM category is then determined using Ohio EPA’s breakpoints (Mack 2000).

ORAM Category 3 - 10 points
ORAM Category 2b - 7 points
ORAM Category 2a - 3 points
ORAM Category 1 - 1 point

Stream Quality

Point are assigned according to the weighted average stream biological assessment score/category within a catchment. To get the weighted average score, points representing assessment sites were joined to the appropriate stream segment and “walked” updstream until the next assessment point is encountered. This allows assessment data to get applied to a stretch of stream rather than just a single point. The length is then used as a weigting factor for the weighted average calculation.

Network Walking

WITH walked AS (
WITH RECURSIVE walk_network(wkb_geometry,
reference_stream_id,
ogc_fid,
“year”,
“type”,
score,
narr,
x1,
y1,
x2,
y2) AS (
SELECT a.wkb_geometry,
a.reference_stream_id,
a.ogc_fid,
b.”year”,
b.”type”,
b.score,
b.narr,
a.x1,
a.y1,
a.x2,
a.y2
FROM nr_sensitivity.all_stream_data_merged_to_segment a
LEFT JOIN nr_sensitivity.bio_narrative_to_streams b ON a.ogc_fid = b.ogc_fid
WHERE a.reference_stream_id IS NOT NULL
UNION ALL
SELECT n.wkb_geometry,
w.reference_stream_id,
n.ogc_fid,
w.”year”,
w.”type”,
w.score,
w.narr,
n.x1,
n.y1,
n.x2,
n.y2
FROM nr_sensitivity.all_stream_data_merged_to_segment n,
walk_network w
WHERE n.x2 = w.x1 AND n.y2 = w.y1 AND n.hhei_score IS NULL
)
SELECT walk_network.wkb_geometry,
walk_network.ogc_fid,
walk_network.reference_stream_id,
walk_network.”type”,
walk_network.score,
walk_network.”year”,
walk_network.narr,
walk_network.x1,
walk_network.x2,
walk_network.y1,
walk_network.y2
FROM walk_network
ORDER BY walk_network.reference_stream_id, walk_network.ogc_fid
)
SELECT a.ogc_fid,
a.wkb_geometry,
a.type_code,
a.network,
a.x1,
a.y1,
a.x2,
a.y2,
a.tostream,
b.reference_stream_id,
b.score,
CASE
WHEN b.”type” = ‘cm hmfei’ THEN b.”year”
ELSE c.”year”
END
AS applied_bio_assess_year,
CASE
WHEN b.”type” = ‘cm hmfei’ THEN b.”type”
ELSE c.”type”
END
AS applied_bio_assess_type,
CASE
WHEN b.”type” = ‘cm hmfei’ THEN b.score
ELSE c.score
END
AS applied_bio_assess_score,
CASE
WHEN b.”type” = ‘cm hmfei’ THEN b.narr
ELSE c.narr
END
AS applied_bio_assess_narr
FROM nr_sensitivity.all_stream_data_merged_to_segment a
LEFT JOIN walked b ON a.ogc_fid = b.ogc_fid
LEFT JOIN nr_sensitivity.bio_narrative_to_streams c ON a.ogc_fid = c.ogc_fid
;




Stream quality metric points are assigned as follows:

Biology Narrative is “Exceptional” or “Very Good” - 10 points
Biology Narrative is “Good” - 7 points
Biology Narrative is “Marginally Good” - 5 points
Biology Narrative is “Fair” - 3 points
Biology Narrative is “Poor” or “Very Poor” - 1 point

Rare Features

Points for this metric are assigned based on the occurrence of rare taxa within a catchment. Each taxon is only counted once per catchment it occurs in even though there may be multiple individual records for it. Conservation status assignment generally follows the Natureserve methodology (Natureserve citation).

Taxa of Global Conservation Concern (G1, G2, G3) - 10 points
Ohio endangered taxa - 7 points
Ohio threatened taxa - 5 points
Ohio potentially threatened taxa - 3 points
Cleveland Metroparks rare taxa (P1, P2) - 1 point

Core Habitat

Points for core habitat are assigned based on the percentage of the catchment that falls within core (i.e., buffered) areas. These core habitat areas were delineate and mapped by Cleveland Metroparks’ GIS staff (need to provide more background/details on this).

Results

nr.index.sub1 <- nr.index[which (nr.index$area_acres < 500),]
nr.index.sub1 <- nr.index.sub1 %>%
  mutate(size_cat = case_when (area_acres >= 200 ~ 'large',
                             area_acres >= 100 ~ 'medium',
                             .default = 'small'))
ggplot(nr.index.sub1, aes(x = area_acres)) +
  geom_histogram (colour = "black", fill = "white", binwidth = 20) +
  ggtitle("Distribution of Catchment Size") +
  xlab("")

ggplot(nr.index.sub1, aes(x = sqrt(area_acres))) +
  geom_histogram (colour = "black", fill = "white", binwidth = 1) +
  ggtitle("Distribution of Catchment Size") +
  xlab("")

ggplot(nr.index, aes(x = nr_value_index)) +
  geom_histogram (colour = "black", fill = "white", binwidth = 2) +
  ggtitle("Distribution of NR Index Values") +
  xlab("")
## Don't know how to automatically pick scale for object of type <integer64>.
## Defaulting to continuous.

ggplot(nr.index, aes(nr_value_index_narrative)) +
  geom_bar(colour = "black", fill = "white", stat = "count") +
  scale_x_discrete(limits=c("high value", "moderately high value", "moderate value", "limited value")) +
  ggtitle("Number of Catchments by Value Rating") +
  xlab("")

ggplot(nr.index, aes(x = nr_value_index_narrative, y = sqrt(area_acres))) +
  geom_bar(stat = "identity") +
  scale_x_discrete(limits=c("high value", "moderately high value", "moderate value", "limited value")) +
  ggtitle("Acres by Value Rating") +
  xlab("")

ggplot(nr.index, aes(x = area_acres, y = nr_value_index)) +
  geom_point(aes(shape = factor(nr_value_index_narrative))) +
  geom_smooth(method=lm)
## Don't know how to automatically pick scale for object of type <integer64>.
## Defaulting to continuous.
## `geom_smooth()` using formula = 'y ~ x'

Let’s remove that huge Bradley Woods cathcment and plto again

ggplot(nr.index.sub1, aes(x = sqrt(area_acres), y = nr_value_index)) +
  geom_point(aes(shape = factor(nr_value_index_narrative))) +
  geom_smooth(method=lm)
## Don't know how to automatically pick scale for object of type <integer64>.
## Defaulting to continuous.
## `geom_smooth()` using formula = 'y ~ x'

ggplot(nr.index.sub1, aes(x=size_cat, y=nr_value_index)) + geom_boxplot() + theme(axis.text.x = element_text(angle=45, vjust=0.5)) + xlab(“Size Category”) + ylab(“Index Score”) + stat_n_text()